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Care seeking, complementary therapy and herbal medicine use among people with type 2 diabetes and cardiovascular disease: CAMELOT phase II: surveying for diversity.


CAMELOT (complementary and alternative medicine, economics, lifestyle and other therapeutic approaches for chronic conditions) was a three year (2008-2011) project designed to enhance our understanding of the use of complementary and alternative medicines (CAM) in Australia. Accordingly the study investigated the drivers, costs, barriers and benefits of CAM use, and the relationship between the use of CAM and conventional medicine among Australians with type 2 diabetes mellitus (DM) or/and cardiovascular disease (CVD). Elsewhere we discussed the background and significance of the study and described the ethnographic research, from which we generated background and qualitative data to inform a survey which we characterise as Phase I of the study (Manderson 2012).

In this article we discuss the Phase II research design, data collection methods and respondent characteristics. We profile and compare the demographic, health status and care seeking characteristics of a general population of people with DM and CVD, and the subgroup of people using naturopathy and herbal medicine practitioners. Our aim in providing this level of detail is to encourage rigorous research on CAM to ensure a strong evidence base to inform health policy and practice, so enhancing self management and health outcomes.

Survival rates from CVD and the prevalence of DM are increasing to near epidemic proportions, making these two of the most significant chronic conditions in Australia, representing 16.1% and 6.6% of Australia's disease burden (AIHW 2010). The majority of older Australians have chronic conditions, yet few studies examine the use of CAM following diagnosis of disease or the combined use of CAM and prescribed medication and behavioural practices. There is poor understanding of variations in conventional and CAM practices among population subgroups, including by people with DM or CVD. This lack of information has important policy implications, given the growing use of CAM therapies by consumers and GPs (e.g. acupuncture).

CAM is variably defined as referring to therapies or modalities usually considered alternative or complementary to the dominant healthcare system (O'Connor 1997). The products and practitioners considered under the CAM umbrella are diverse, including naturopathy, homeopathy, herbal medicine, nutritional medicine (including vitamin or mineral supplements), aromatherapy, reflexology, massage therapies, Ayurveda, acupuncture, Chinese medicine, spiritual healing, meditation, Tai chi, yoga and many others. It has been suggested that chronic disease management could be enhanced through greater integration of CAM and primary health care (Willison 2005); that people who use CAM are more likely to participate in conventional medical care than those who do not (2006); and that care from CAM practitioners may support rather than compromise adherence to prescribed medication and other self care practices (Grzywacz 2005, Arcury 2006). Consumers visit CAM practitioners for a number of reasons, including to receive 'holistic care' (i.e. attention to the whole person); treatment and support of chronic conditions; maintenance of wellbeing; mitigation of pharmaceutical side effects; and in appreciation of the relationship with, time spent by, and setting of the CAM practitioner (Paterson 2008, Lin 2009).

Despite evidence of increased use of a wide range of CAM by different patient groups, relatively little is known about the determinants of expenditure on CAM, the choice of practitioner and regularity of consultation, or the role of social networks in establishing and sustaining patterns of care (Nahin 2005, Kemper 2008, Lafferty 2008, Nguyen 2010). Our research was designed to elucidate how people engaged in diverse health practices, and to explore the association between engagement in these practices and adherence to conventional medical advice. We were interested in examining how individuals use particular health modalities, how they integrated these in everyday self care, and how ideas about CAM informed the management of particular medical conditions.

Phase II of the CAMELOT study was designed to provide generalisable quantitative findings to complement the qualitative first phase. In this phase we were able to investigate further the relevance of social, locational, economic and cultural factors to the concurrent or alternative use of CAM, and the relationship of CAM use to treatment adherence and self management among people with CVD and/or DM.


Study design

CAMELOT used an interdisciplinary (anthropology, psychology, economics, epidemiology and health policy), multiphased, mixed methods design. Phase II, conducted in 2010 and reported here, drew from Phase I ethnographic research (Manderson 2012) and document reviews to develop a self administered questionnaire for data collection. Analysis of Phase II draws on the rich interview data from Phase I to contextualise findings.

The study was overseen by the CAMELOT Reference Group, which convened biannually and comprised representatives from patient and consumer advocacy groups and from CAM and medical practitioner organisations (see acknowledgements). Ethics approval was granted by the Monash University Human Research Ethics Committee.

Questionnaire development

The questionnaire was informed by the study objectives, preliminary analysis of the Phase I qualitative data, an ongoing review of the literature and a review of previously used survey instruments on CAM, illness perception, chronic condition management and quality of life (Moss-Morris 200, Xue 2007, Taylor 2008, Williamson 2008, AQoL 2009). From this research the most salient and important themes for the questionnaire were identified and new questions were drafted where relevant scales or questions did not already exist (Table 1). We aimed to produce a questionnaire which could be completed on hard copy or online in 20-25 minutes (see for a hardcopy version). An iterative process of pretesting, piloting, revising and re-drafting of questions occurred with input from all members of the research team, members of the public and the Reference Group. Once the hardcopy questionnaire was finalised it was transferred to an online format using Survey Methods Professional internet based survey software. The online format made use of 'skip logic' controls so respondents were directed to (miss) particular questions depending on their answer to previous questions.

The piloting process involved testing each version of the survey, with a focus on input from people with DM and/or CVD. The pilot group were sourced from the Phase I participant pool (people living with DM or/and CVD and using CAM) or from diabetes or heart support groups. Completed paper based pilot questionnaires were cross checked to assess respondents' understanding of the questions and for thoroughness of completion. Instructions or questions that caused difficulty were modified. Respondents were asked to provide feedback on the length of time taken to complete the survey, their interest in the subject matter and any questions they found difficult. We were especially concerned with the length of the questionnaire (in final form comprising 71 questions often multiple part), but the pilot group reported no issue about length. The online questionnaire was piloted separately to ensure its comprehension and its usability.

The questionnaire was in five sections for collection of demographic and health related data: Getting information and use of health services; Use of complementary and alternative medicine; Health insurance; Your health, lifestyle and preferences; and About you. The fourth section incorporated the four dimension Assessment of Quality of Life (AQoL-4D) questionnaire (Q48-54), which was chosen as the only quality of life instrument which incorporated Australian preference weights (AQoL 2009). The dimensions measured by AQoL are: independent living, relationships, senses and mental health. The AQoL-4D was included to provide an objective, quantifiable measure of respondents' quality of life to be considered in relation to other question responses, from which a utility score (individual's satisfaction) could be calculated. Individual AQoL questions could also be analysed to provide information about respondents' networks and levels of (dis)ability.


For inclusion, questionnaire respondents needed to be at least 18 years of age, have an adequate level of English comprehension and be diagnosed with DM and/or any CVD (including high blood pressure, high cholesterol, angina, heart bypass or similar, stroke). Respondents were residents of the Australian state of Victoria or a Victorian border town, i.e. surveying people from the same geographic area as the Phase I qualitative research and therefore allowing the use of both data sets in analysis. We aimed for a response rate of 2000 valid survey returns, a sufficiently large number to enable multilevel comparison of subgroups within the respondent sample.

Participant recruitment and data collection

We used diverse recruiting methods to ensure diversity of respondents, involving many types of organisations and community groups whose members included people of our target demographic. With support from Diabetes Australia (DA) and the National Diabetes Services Scheme (NDSS), the Victorian mailing list of 67 163 registrants with DM, who had agreed to be contacted for research purposes (approximately 35% of Victorian registrants), was provided confidentially to a third party mailing house engaged by the research team. Similarly, with permission, the Victorian mailing list for Heart Support Australia (HS-A) was provided confidentially to the mailing house. Identifiable registrant information was not provided to the researchers.

Questionnaires with reply paid return envelopes and a web link to the online survey, were posted to a random sample of 10 000 Victorian NDSS registrants (registered at the end of 2009). In the absence of a network of support groups or consumer organisations for people with CVD (such as exists for diabetes), it was difficult to directly access the CVD population. However all people on the HS-A Victorian mailing list (n=672), an unknown number of whom were spouses, were sent questionnaires. All questionnaires sent to mailing list recipients were individually barcoded so survey returns could be matched, reminders sent if necessary, and de-identified information made available on the basic characteristics of responders and non responders.

Around 1490 questionnaires were distributed via other avenues: through the networks of Reference Group member organisations; direct contact with other CAM and medical professional or consumer representative organisations; media releases (leading to print and radio interviews); paid advertising in the Victorian editions of Senior News and Fifty-Plus News; a project Facebook page; the distribution of notices via public noticeboards, local councils (libraries, town halls), gyms, weight loss and service clubs, and by distributing information flyers and questionnaire packets (with reply paid envelopes) at a four day commercial Body, Mind and Spirit Convention. This was labour intensive but provided access to potential respondents who may not have been members of NDSS or HS-A, thereby broadening the diversity of the sample. Practitioner information kits including instruction/ information letter, recruitment poster, reply paid flyers and sample questionnaires were also distributed, mostly to CAM practitioners and diabetes educators.

The survey was available online from 3 April to 15 July 2010 and postal surveys were mailed on 16 April 2010, returns closing on 15 July 2010. The online survey was available by direct web link or through a portal on the CAMELOT website. A free call telephone support line was established for survey respondents to seek assistance, if needed, when completing the questionnaire.

To increase the response rate all respondents who returned completed questionnaires by 15 July 2010 were placed in a prize draw to win one of 100 AU$50 shopping vouchers. Winners could remain anonymous by indicating on their survey that they wished to donate their prize to DA-V or HS-A. On the close of the survey, 100 valid surveys were randomly selected and winners notified as appropriate. The 'prize' strategy, envisaged as an incentive to encourage participation, appeared to be unnecessary the majority of winners, when contacted, had little idea that they had been placed in a prize draw, but completed the survey because it had been posted to them.

Data entry, cleaning and quality control

The mailing house which distributed the questionnaires also received the completed postal responses and captured their data. Valid surveys were electronically scanned then double entered by two different computer operators with the assistance of a data capture overlay template. Any discrepancies in values keyed by operators in the two data capture passes were adjudicated by a supervisor. Daily return counts showed that 83% (2501 of the 3009 total postal returns) were received in the first month of data collection.

On close of the survey the data was loaded into PASW Statistics v.18 for cleaning and quality control. All modifications to the data during cleaning were recorded and the original raw data set retained. Data cleaning addressed multiple responses to single response questions, outliers, coding of 'other' responses and response inconsistency. Checking consistency between questions entailed taking a 'whole of survey' approach by checking other responses in the same survey, often by sighting the original survey image. For example where a respondent indicated they had not used CAM, but elsewhere indicated they had used CAM (at questions relating to specifically named CAM modalities), their original negative response was changed to positive. Missing or negative responses to 'ever having used CAM' were altered to the affirmative for 3.5% of the postal sample. Expenditure question outliers (those over $500 per month) where checked to ensure accurate data capture and if accurate and respondent had agreed, they were contacted to verify their response. Where multiple responses were provided for single response questions the most conservative option was retained.

Data received from online questionnaires needed far less 'cleaning' because automatic controls prevented multiple responses to single response questions and skip logics directed respondents to skip questions based on previous responses. However the presence of the online skip logic may have led to under reporting of CAM use when participants were not given opportunity to respond to CAM use questions.

In contrast, contrary to instructions, postal survey respondents, for whom all questions were visible, were able to respond to specific questions about their use of CAM, even if they initially indicated never having used a CAM product or visited a CAM practitioner.

The quality control exercise involved checking a random sample (5%) of valid surveys for data capture accuracy, data cleaning thoroughness (including recording of modifications made) and checking for whole of survey consistency.


The statistical analysis software IBM SPSS v.19 was used to generate the descriptive statistics used in this paper. Tables 4 and 5 compare the whole of survey sample with a subsample of 116 respondents using Western herbal medicine (WHM) and naturopathy practitioners. These respondents are also counted in the whole sample. Naturopathy and WHM practitioners are grouped together due to their often similarity of practice; naturopaths in Australia are frequently trained to prescribe herbal medicines. The comparison of the demographic and health status characteristics of the subsample using naturopathy and WHM practitioners against the whole sample provides a unique profile of these CAM users within a general chronic disease population. Unless specified otherwise, statistics relating to CAM use refer to use in the previous 12 months. All currency values are in Australian dollars.


Response rate and respondent distribution

A total of 2915 (290 online and 2625 postal) valid survey responses were received from a total of3385 returns (Table 2). A map of the spread of survey respondents is at <>. The number of valid respondents (n=2915) exceeded the 2000 sought. Of the respondents who were recruited through the mailout to NDSS registrants, the proportion of males and females in ten year age brackets are broadly representative of the NDSS population, as shown in Table 3. Just 1.3% (n=47) of respondents were recruited via CAM practitioners, too few to bias results relating to prevalence of CAM use.

CAM use

Almost half (n=1396, 47.9%) of respondents had used CAM in the past (ever), and 42.8% (n=1247) had used CAM in the last 12 months. Of this group 53.8% (n=671) visited CAM practitioners. Western herbal medicine (WHM) was used by 324 respondents, including medicinal herbal teas, tablets and tinctures. However, just 12 respondents visited herbal medicine practitioners, and 110 visited a naturopath-of which 59% (n=65) used WHM (and 11% unknown). CAM products were used by 1180 respondents. While 27.7% (n=346) of the whole sample used CAM for the treatment of CVD or DM, 57.8% of naturopathy/WHM practitioner users did so. Table 4 shows the number of respondents who consulted with various types of CAM practitioners, for any reason.

Respondent characteristics

Respondent demographic characteristics are outlined in Table 5 and care seeking and health status characteristics in Table 6. The majority of respondents were male (n=1583), had a mean age of 65.1 years (SD=11.1, range 20-96 years), married (68.0%, n=1983) and lived with their family (76.2%, n=2220). Most respondents identified their employment status as retired (60.4%, n=1762). Eighteen identified as Aboriginal and/ or Torres Strait Islander (0.7%), and 5.7% said English was not their first language (n=164). People with an Indigenous background were lower in the survey sample (0.6%) compared with the NDSS Australian database (1.8%). Although the proportion of respondents born out of Australia was slightly higher than within the Australian population (30.5% cf. 29.1%), 89.7% of all participants spoke English as their only language compared with 78.5% of the total population (2006 Australian Census).

The majority of respondents were of low socioeconomic status. The most common income bracket was AU$0-$25,000 (37.2%, n=1083), most had a health benefits card (indicating that their income level was low and that they were entitled to state subvention of health and pharmaceutical costs), and for 15.2% (n=443) of the sample, the highest level of education was year 8 or below (i.e. left school before 15 years of age).

The majority of respondents had been diagnosed with DM or CVD for five or more years (n=2258, 77.5%) and were taking pharmaceuticals prescribed by a medical doctor (n=2800, 96.1%) (Table 6). The majority were overweight or obese (n=2246, 77.0%), with body mass index (BMI) ranging from 15.8 to 70.3 (mean 30.5, SD 6.5), as calculated using self reported height and weight. Few were smokers (8.3%). The most commonly reported cardiovascular condition was high blood pressure (62.3%, n=1816).

Naturopathy and WHM user profile

The respondents who used a naturopathy or WHM practitioner diverged significantly from the main survey sample in numerous ways. They were more likely than the whole sample to be female (69%), live in a major city, have post secondary education, private health insurance, higher income and work in a professional capacity, and were slightly younger (average age 60.9 years SD 10.6). These results align with findings from other research (Hill 2006). Even so the majority of users of naturopathy/ WHM practitioners had a low household income of under AU$50,000 (n=60, 51.7%), were not working (50.8%) and had a health benefits card (64.7%).

In terms of care seeking and health status, the users of naturopathy and WHM practitioners were significantly more likely than the whole sample to exercise, have CVD or risk factors (high blood pressure or high cholesterol), anxiety or depression, food allergy or intolerance, take fewer prescribed pharmaceutical medications and more CAM products, and visit a greater number of doctors. There was no significant difference between the groups in frequency of visiting a general medical practitioner (GP), body mass index, self reported health, or number of comorbid chronic conditions in addition to DM or CVD.

The naturopathy/WHM practitioner users spent a median monthly amount of $60 on CAM practitioners and $58 on CAM product, compared with $20 and $30 for the whole of sample. The cost of accessing CAM products or practitioners was a barrier to significantly more users of naturopathy/WHM practitioners than to the whole sample group (Table 6).


Forty-three percent of all survey respondents self reported use of CAM product or practitioners in the 12 months prior to the survey; 23.0% had visited CAM practitioners, most commonly chiropractors or massage therapists. If respondents recruited via CAM practitioners are excluded, then 41.3% of remaining participants used CAM in the 12 month period, and 21.9% visited CAM practitioners (n=627). Our results are from a group of self selecting respondents (22% response rate), so there is potential that the prevalence of CAM use is overestimated if people who had used CAM were more interested in completing the survey than non CAM users. Nevertheless the data indicated trends in the characteristics of users of naturopathy and WHM practitioners which aligned with other research findings (Hill 2006).

However our responder group of chronic disease patients who use CAM have a somewhat different sociodemographic profile from CAM users in the general population. Socioeconomic disadvantage is a significant risk factor in the development of chronic disease including CVD and DM (AIHW 2006). It is not surprising that much of our sample came from lower socioeconomic groups; 62.5% (n=779) of CAM users had a household income [less than or equal to] $50,000. Although many were on pensions or benefits because they had retired, a third of CAM users were working, of which 38% reported low household income ([less than or equal to] $50,000). This contradicts the common held notion that CAM is 'a middle-class commodity' (Palmer 2000 p. 186). One of our tasks is now to tease out the interrelationships of class, gender, disease status and health care, using national data to assess for comparability and so the representativeness of our survey.

We noted low smoking rates, hinting perhaps that our respondents, motivated to participate in the study, were relatively health conscious and drawing attention to the difficulty of getting a representative sample of chronic disease patients. This is especially so because of the relative ease of accessing DM participants through the DA-V; HSA has a much smaller membership and few people with cardiovascular conditions have access to ongoing support groups except when, as a result of co-morbidity, they access other programs (e.g. diabetes, arthritis, weight loss, mental health).

Given the broad scope of the CAMELOT project, the findings will be published within numerous articles to participants, CAM and medical providers. This paper, which detailed the Phase II research protocol and characteristics of the survey respondents, and the Phase I paper previously published (Manderson 2012), provide points of reference for the forthcoming publications.

A paper exploring Phase I data on the perspectives and experiences of CAM users with CVD and DM, and the knowledge and perspectives of the herbal medicine and other CAM and medical practitioners who treat these people, will enable greater elucidation of the WHM and naturopathy user profile provided here.


The CAMELOT study collected a large qualitative and quantitative dataset on CAM use and non use in a population with chronic illness. This presented tremendous opportunities for investigative research by this multidisciplinary team and reflected the diverse input of our different perspectives and concerns, and the commitment and advice of the Reference Group. The data offers rich information into the demographics of people with DM and/or CVD and their combined use of different kinds of therapies and treatment modalities. The questionnaire provided data on illness perception, quality of life, social and economic background, and the impact of using CAM products and providers. These give a unique opportunity to provide policy relevant information on the costs, benefits and impact of CAM use on the health of those with chronic illness. Future publications will report in all of these areas.

Competing interests

The authors declare no competing interests.


We wish to thank Pauline McCabe, Stephen Bunker, Milica Markovic, Charlie Xue, Jennifer Moral, Victoria Team, Nalika Unantenne and CAMELOT Reference Group representatives from: Australasian Integrative Medicine Association, Australian Homeopathic Association, Australian Naturopathic Practitioners' Association, Australian Natural Therapists Association, Australian Traditional-Medicine Society, Chronic Illness Alliance, Diabetes Australia-Victoria, General Practice Victoria, Heart Support Australia, Health Issues Centre, National Herbalists Association of Australia.


National Health and Medical Research Council grant number 491171.


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Lenore Manderson BA Asian Stud (Hons) PhD FASSA FWAAS, CAMELOT Project, School of Psychology and Psychiatry, Monash University, Caulfield Victoria Australia, email: Lenore Manderson is Professor of Medical

Anthropology at Monash University. Her research interests include social exclusion and marginality and the social determinants of infectious and chronic disease in Australia, Southeast and East Asia and Africa.

Brian Oldenburg BSc (Hons) MPsych PhD, CAMELOT Project, Department of Epidemiology and Preventive Medicine, Monash University, The Alfred Centre, Victoria Australia, email: brian. Brian Oldenburg is Professor and Chair, International Public Health Unit at Monash University. His research, spanning the health social and behavioural sciences and public health, is focused on health systems, health policy, global health and the primary and secondary prevention of non communicable diseases and associated social and behavioural risk factors across the life course.

Vivian Lin BA MPH DrPH, CAMELOT Project, School of Public Health, La Trobe University, Victoria Australia, email: Vivian Lin is Professor of Public Health at La Trobe University and works on policy, planning and program development across a wide range of health issues. She was on the Board of the Chinese Health Foundation of Australia and was inaugural President of the Chinese Medicine Registration Board of Victoria 2000-2009. She is the Vice President for Scientific Affairs for the International Union of Health Promotion and Education.

Bruce Hollingsworth BA (Hons) MSc PhD, CAMELOT Project, Division of Health Research, Lancaster University UK, email: Bruce Hollingsworth is Professor of Health Economics at Lancaster University and was previously Director of the Centre for Health Economics at Monash University. His work focuses on efficiency measurements with respect to the production of health care, social determinants or health and translation of research into practice.

Maximilian de Courten MPH MD, CAMELOT Project, Copenhagen School of Global Health, University of Copenhagen, Denmark, email: Maximilian de Courten is the inaugural Professor of Global Public Health at the Copenhagen School of Global Health, the University of Copenhagen, and a specialist in global public health, chronic disease surveillance and prevention and community based interventions. He previously worked at Monash University and at WHO in Geneva and the South Pacific.

Rachel Canaway BHSc MSocHlth MNHAA, CAMELOT Project, School of Psychology and Psychiatry, Monash University, Caulfield Victoria Australia, email: Rachel Canaway is a Research Fellow at Monash University and has worked as a naturopath and massage therapist. Rachel's research focuses on natural therapies, medical pluralism, professional and policy issues relating to CAM and the paradigmatic tensions arising between different 'types' of health care providers and users.

Jean Spinks BPharm MPH MHSc, CAMELOT Project, Centre for Health Economics, Monash University, Clayton, Victoria, Australia, email: Jean Spinks is a Research Fellow in the Centre for Health Economics at Monash University. Her research interests include health system design and evaluation, with a special interest in pharmaceutical systems. She has worked in Australia, Papua New Guinea, Indonesia and Tonga. She is completing her doctorate on economic implications of complementary and alternative medicine use, particularly in people with type 2 diabetes and cardiovascular disease, with the CAMELOT project.

Corresponding author: Rachel Canaway, CAMELOT Project, School of Psychology and Psychiatry, Monash University, PO Box 197, Caulfield East, Victoria Australia 3145 Email:
Table 1: Questions which shaped the
survey design

* What are the influences (drivers / motivators) of the
use of CAM?

* What costs are associated with CAM use?

* What are the perceived benefits of CAM use?

* What are barriers to CAM use?

* Are those who see CAM practitioners more
successful at sustaining healthy behaviour
modifications than those who do not?

* Are people on higher numbers of medications less
likely to use CAM?

* How does the laypersons' understanding of
health, disease or illness promote alignment with
biomedical or holistic treatments?

* How do information pathways shape the
supplementary or alternative use of CAM?

* How do people exercise control over their health?

Table 2: Questionnaire distribution and return rate

                               Total no.
                   No.        returned or    Valid     % return
Postal          distributed    attempted     surveys

NDSS              10 000          2572        2203        22

HSA                 672           181          166        25

Miscellaneous      ~1490          256          256        17

Total postal      ~12,162         3009        2625        22

Total online        N/A           376          290       N/A

Grand total                       3385        2915       N/A

Table 3: Comparison of survey respondents recruited via NDSS with
Victorian and Australian NDSS samples

            NDSS Survey respondents
            n (%)

Age group   Female              Male

18-29       3 (0.1)             3 (0.1)

30-39       20 (1.0)            5 (0.2)

40-49       82 (3.7)            67 (3.0)

50-59       206 (9.3)           266 (12.0)

60-69       308 (14.0)          477 (21.7)

70-79       222 (10.1)          343 (15.6)

80-89       83 (3.8)            107 (4.9)

90+         7 (0.3)             4 (0.2)

Subtotals   931 (42.3)          1272 (57.7)

Totals                   2203

            NDSS Victoria
            n (%)

Age group   Female                   Male

18-29       420 (0.2)                264 (0.1)

30-39       2416 (1.2)               1913 (0.9)

40-49       8100 (4.0)               8061 (4.0)

50-59       15 807 (7.8)             20 116 (10.0)

60-69       23 356 (11.6)            31457 (15.6)

70-79       23 593 (11.7)            27 424 (13.6)

80-89       16 797 (8.3)             15 358 (7.6)

90+         4019 (2.0)               2443 (1.2)

Subtotals   94 508 (46.9)            107 036 (53.1)

Totals      201 544         201544

            NDSS Australia
            n (%)

Age group   Female                    Male

18-29       2153 (0.3)                1376 (0.2)

30-39       11 535 (1.4)              8450 (1.0)

40-49       38 147 (4.6)              34 899 (4.2)

50-59       69 058 (8.3)              85 507 (10.3)

60-69       96 561 (11.7)             133 310 (16.1)

70-79       91094 (11.0)              109 065 (13.2)

80-89       63 503 (7.7)              58480 (7.1)

90+         15 201 (1.8)              9557 (1.2)

Subtotals   387 252 (46.8)            440 644 (53.2)

Totals                       827896

Table 4: CAM practitioners visited in last 12

                                                 Number of
CAM practitioner                                respondents

Chiropractor of osteopath                           308
Massage therapist or similar                        308
Nutritionist                                        177
Integrative GP (medical doctor who uses             117
  CAM therapies)
Naturopath                                          110
Acupuncturist                                       95
Energy healer: includes Reiki, Pranic healer,       63
Chinese or Oriental medicine                        42
Homeopath                                           26
Spiritual healer, meditation, prayer group          21
Music, art or colour therapist                      20
Hypnotherapist                                      16
Western herbalist                                   12
Indigenous or traditional healer/therapist           4
Other CAM practitioners                             33

Table 5: Demographic characteristics: naturopathy/WHM users vs
whole of survey

Category             Detail

Gender **            Female


Age group            20-49
(years) *            50-59

Post secondary       Yes
education **         No

Household income *   $0 - $50,000
                     $150,001 +

Employment *         Not in labour force
                     Working full or part time

Occupation ** -      Professional
current or
previous             Manager
                     Clerical or administrative
                     Technician or trade
                     Machine operator & labourer
                     Community/ personal service
                     Sales worker
                     Other & never worked

ASGC remoteness      Major city
category *           Inner regional
                     Outer regional

Born in Australia    Yes

Health benefits      Yes
card *               No

Private health       Yes
insurance *          No

                     Entire CAMELOT
                         sample       Naturopathy or
                         n=2915          WHM user
Category                 n (%)            n (%)

Gender **             1307 (44.8)       80 (69.0)

                      1583 (54.3)       31 (26.7)

Age group              234 (8.0)         10 (8.6)
(years) *              627 (21.5)       38 (32.8)
                       999 (34.3)       36 (31.0)
                       745 (25.6)       25 (21.6)
                       284 (9.7)         2 (1.7)

Post secondary        1195 (41.0)       67 (57.8)
education **          1654 (56.7)       44 (37.9)

Household income *    1821 (62.5)       60 (51.7)
                       564 (19.3)       29 (25.0)
                       205 (7.0)        15 (13.0)

Employment *          1948 (66.8)       59 (50.8)
                       926 (31.8)       52 (44.8)

Occupation ** -        680 (23.3)       43 (37.1)
current or
previous               481 (16.5)       15 (12.9)
                       455 (15.6)       22 (19.0)
                       323 (11.1)        4 (3.4)
                       360 (12.3)        8 (6.9)
                       179 (6.1)         11 (9.5)
                       156 (5.4)         4 (3.4)
                       168 (5.8)         4 (3.4)

ASGC remoteness       1741 (59.7)       82 (70.7)
category *             956 (32.8)       28 (24.1)
                       215 (7.4)         6 (4.3)
                        2 (0.1)          1 (0.9)

Born in Australia     1963 (67.3)       77 (66.4)
                       890 (30.5)       34 (29.3)

Health benefits       1816 (62.3)       59 (50.9)
card *                 979 (33.6)       50 (43.1)

Private health        1526 (52.3)       75 (64.7)
insurance *           1341 (46.0)       36 (31.0)

* p<0.05>0.001
** p<0.001

Table 6: Care seeking and health status: naturopathy/WHM users vs
whole of survey

Category                 Detail

Diagnosis                DM only
                         DM & CVD risk factors ([double dagger])
                         DM & CVD ([dagger])
                         CVD only ([dagger])
                         CVD risk factors only ([double dagger])

Chronic conditions       Anxiety or depression **
had in past 5 years      Cancer
                         Food allergy or intolerance **
                         Respiratory conditions
                         Other (includes arthritis)

Number of chronic        None (0)
conditions in past       1-2
5 years In addition      3-4
to DM or CVD             5+

Smoker                   Yes

Exercised in the         Yes
last 2 weeks *           No

Body mass index          Underweight (<18.5)
                         Normal (18.5<25)
                         Overweight (25<30)
                         Obese (30<35)
                         Severely obese (35<40)
                         Morbidly obese (40+)

Self reported health     Excellent
                         Very good

Number of different      1-2
doctors or medical       3-4
specialists visited      5 or more
in last 12 months **

Frequency of visiting    Less than once yearly
GP                       Every 3-6 months
                         At least monthly

Number of different      0 (none)
medications taken        1-3
prescribed by a          4-6
medical doctor for       7 or more
any reason **

Used any CAM last        Yes
12 months for any        No

Cost prevented           Yes
visit to CAM             No
practitioner **

Cost prevented           Yes
buying CAM products **   No

                         Entire CAMELOT   Naturopathy or WHM
                             sample              user
                             n=2915             n (%)
Category                     n (%)

Diagnosis                  513 (17.6)         15 (12.9)
                          1317 (45.2)         50 (43.1)
                           819 (28.1)         19 (16.4)
                           175 (6.0)          16 (13.8)
                            91 (3.1)          16 (13.8)

Chronic conditions         745 (25.6)         44 (37.9)
had in past 5 years        249 (8.5)           6 (7.8)
                           222 (7.6)          21 (18.1)
                           344 (11.8)          9 (7.8)
                           624 (21.4)         16 (22.5)

Number of chronic         1143 (39.2)         32 (27.6)
conditions in past        1320 (45.3)         63 (54.3)
5 years In addition        171 (5.9)           7 (6.0)
to DM or CVD                45 (1.5)           3 (2.6)

Smoker                     243 (8.3)           8 (6.9)
                          2631 (90.3)         104 (89.7)

Exercised in the          2068 (70.9)         91 (78.4)
last 2 weeks *             808 (27.7)         22 (19.0)

Body mass index             12 (0.4)           0 (0.0)
                           481 (16.5)         27 (23.3)
                           995 (34.1)         41 (35.3)
                           703 (24.1)         18 (15.5)
                           323 (11.1)         12 (10.3)
                           225 (7.7)           11 (9.5)

Self reported health        51 (1.7)           2 (1.7)
                           539 (18.5)         20 (17.2)
                          1230 (42.2)         52 (44.8)
                           848 (29.1)         35 (30.2)
                           201 (6.9)           4 (3.4)

Number of different       1599 (54.9)         59 (50.9)
doctors or medical         816 (28.0)         33 (28.4)
specialists visited        338 (11.6)         19 (16.4)
in last 12 months **

Frequency of visiting      103 (3.5)           7 (6.0)
GP                        2094 (71.8)         82 (70.7)
                           684 (23.5)         26 (22.4)

Number of different         85 (2.9)          15 (12.9)
medications taken          825 (28.3)         44 (37.9)
prescribed by a           1234 (42.3)         39 (33.6)
medical doctor for         741 (25.4)         16 (13.8)
any reason **

Used any CAM last         1247 (42.8)         116 (100%)
12 months for any         1668 (57.2)             -

Cost prevented             613 (21.0)         50 (43.1)
visit to CAM              2159 (74.1)         63 (54.3)
practitioner **

Cost prevented             633 (21.7)         50 (43.1)
buying CAM products **    2163 (74.2)         63 (54.3)

* p [less than or equal to] 0.05>0.001, ** p [less than or equal to]
0.001, ([dagger]) CVD = any cardiovascular condition, includes
angina, atherosclerosis, heart bypass surgery (or similar), stroke.
([double dagger]) CVD risk factors = high blood pressure or high
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Title Annotation:complementary and alternative medicine, economics, lifestyle and other therapeutic approaches for chronic conditions
Author:Manderson, Lenore; Oldenburg, Brian; Lin, Vivian; Hollingsworth, Bruce; de Courten, Maximilian; Cana
Publication:Australian Journal of Herbal Medicine
Article Type:Report
Geographic Code:8AUST
Date:Jun 1, 2012
Previous Article:Editorial.
Next Article:Evidence based practice review: cranberry for urinary tract infection prophylaxis in children at risk of recurrent UTI.

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